# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： sine_hw_position_embedding
Description :
Author : 'li'
date： 2022/6/29
Change Activity:
2022/6/29:
-------------------------------------------------
"""
import math

import torch

from ...model.base import BaseModule


class PositionEmbeddingSineHW(BaseModule):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """

    def __init__(self, num_pos_feats=64, temperature_h=10000, temperature_w=10000, normalize=True, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature_h = temperature_h
        self.temperature_w = temperature_w
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, x, mask=None):
        """

        Args:
            x: with shape (batch_size,channels_number,h,w)
            mask:(batch_size,h,w)

        Returns:

        """
        device = x.device
        if mask is None:
            batch_size, channels_number, h, w = x.shape
            mask = torch.ones((batch_size, h, w))
        mask = mask.to(torch.bool).to(device)
        y_embed = mask.cumsum(1, dtype=torch.float32)
        x_embed = mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_tx = self.temperature_w ** (2 * (torch.div(dim_tx, 2, rounding_mode='trunc')) / self.num_pos_feats)
        pos_x = x_embed[:, :, :, None] / dim_tx
        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_ty = self.temperature_h ** (2 * (torch.div(dim_ty, 2, rounding_mode='trunc')) / self.num_pos_feats)
        pos_y = y_embed[:, :, :, None] / dim_ty
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos
